Mercurial > repos > dchristiany > data_manager_proteore
view data_manager/resource_building.py @ 7:d16a52bf0e5b draft
planemo upload commit d703392579d96e480c6461ce679516b12cefb3de-dirty
author | dchristiany |
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date | Fri, 19 Oct 2018 04:22:37 -0400 |
parents | 9c75521e4a64 |
children | 2f153b41b6fe |
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""" The purpose of this script is to create source files from different databases to be used in other tools """ import os, sys, argparse, requests, time, csv, re from io import BytesIO from zipfile import ZipFile from galaxy.util.json import from_json_string, to_json_string ####################################################################################################### # General functions ####################################################################################################### def unzip(url, output_file): """ Get a zip file content from a link and unzip """ content = requests.get(url) zipfile = ZipFile(BytesIO(content.content)) output_content = "" output_content += zipfile.open(zipfile.namelist()[0]).read() output = open(output_file, "w") output.write(output_content) output.close() def _add_data_table_entry(data_manager_dict, data_table_entry,data_table): data_manager_dict['data_tables'] = data_manager_dict.get('data_tables', {}) data_manager_dict['data_tables'][data_table] = data_manager_dict['data_tables'].get(data_table, []) data_manager_dict['data_tables'][data_table].append(data_table_entry) return data_manager_dict ####################################################################################################### # 1. Human Protein Atlas # - Normal tissue # - Pathology # - Full Atlas ####################################################################################################### def HPA_sources(data_manager_dict, tissue, target_directory): if tissue == "HPA_normal_tissue": tissue_name = "HPA normal tissue" url = "https://www.proteinatlas.org/download/normal_tissue.tsv.zip" elif tissue == "HPA_pathology": tissue_name = "HPA pathology" url = "https://www.proteinatlas.org/download/pathology.tsv.zip" elif tissue == "HPA_full_atlas": tissue_name = "HPA full atlas" url = "https://www.proteinatlas.org/download/proteinatlas.tsv.zip" output_file = tissue +"_"+ time.strftime("%d-%m-%Y") + ".tsv" path = os.path.join(target_directory, output_file) unzip(url, path) print(str(os.path.isfile(path))) tmp=open(path,"r").readlines() tissue_name = tissue_name + " " + time.strftime("%d/%m/%Y") data_table_entry = dict(value = tissue, name = tissue_name, path = path) _add_data_table_entry(data_manager_dict, data_table_entry, "protein_atlas") ####################################################################################################### # 2. Peptide Atlas ####################################################################################################### def peptide_atlas_sources(data_manager_dict, tissue, target_directory): # Define PA Human build released number (here early 2018) atlas_build_id = "472" # Define organism_id (here Human) - to be upraded when other organism added to the project organism_id = "2" # Extract sample_category_id and output filename sample_category_id = tissue.split("-")[0] output_file = tissue.split("-")[1] +"_"+ time.strftime("%d-%m-%Y") + ".tsv" query = "https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/GetPeptides?atlas_build_id=" + \ atlas_build_id + "&display_options=ShowMappings&organism_id= " + \ organism_id + "&sample_category_id=" + sample_category_id + \ "&QUERY_NAME=AT_GetPeptides&output_mode=tsv&apply_action=QUERY" download = requests.get(query) decoded_content = download.content.decode('utf-8') cr = csv.reader(decoded_content.splitlines(), delimiter='\t') #build dictionary by only keeping uniprot accession (not isoform) as key and sum of observations as value uni_dict = build_dictionary(cr) tissue_id = "_".join([atlas_build_id, organism_id, sample_category_id,time.strftime("%d-%m-%Y")]) tissue_value = tissue.split("-")[1] tissue = tissue.split("-")[1] + "_" +time.strftime("%d-%m-%Y") tissue_name = " ".join(tissue_value.split("_")) + " " + time.strftime("%d/%m/%Y") path = os.path.join(target_directory,output_file) with open(path,"wb") as out : w = csv.writer(out,delimiter='\t') w.writerow(["Uniprot_AC","nb_obs"]) w.writerows(uni_dict.items()) data_table_entry = dict(value = path, name = tissue_name, tissue = tissue) _add_data_table_entry(data_manager_dict, data_table_entry, "peptide_atlas") #function to count the number of observations by uniprot id def build_dictionary (csv) : uni_dict = {} for line in csv : if "-" not in line[2] and check_uniprot_access(line[2]) : if line[2] in uni_dict : uni_dict[line[2]] += int(line[4]) else : uni_dict[line[2]] = int(line[4]) return uni_dict #function to check if an id is an uniprot accession number : return True or False- def check_uniprot_access (id) : uniprot_pattern = re.compile("[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}") if uniprot_pattern.match(id) : return True else : return False ####################################################################################################### # 3. ID mapping file ####################################################################################################### import ftplib, gzip csv.field_size_limit(sys.maxsize) # to handle big files def id_mapping_sources (data_manager_dict, species, target_directory) : human = species == "human" species_dict = { "human" : "HUMAN_9606", "mouse" : "MOUSE_10090", "rat" : "RAT_10116" } files=["idmapping_selected.tab.gz","idmapping.dat.gz"] #header if human : tab = [["UniProt-AC","UniProt-ID","GeneID","RefSeq","GI","PDB","GO","PIR","MIM","UniGene","Ensembl_Gene","Ensembl_Transcript","Ensembl_Protein","neXtProt","BioGrid","STRING","KEGG"]] else : tab = [["UniProt-AC","UniProt-ID","GeneID","RefSeq","GI","PDB","GO","PIR","MIM","UniGene","Ensembl_Gene","Ensembl_Transcript","Ensembl_Protein","BioGrid","STRING","KEGG"]] #print("header ok") #selected.tab and keep only ids of interest selected_tab_file=species_dict[species]+"_"+files[0] tab_path = download_from_uniprot_ftp(selected_tab_file,target_directory) with gzip.open(tab_path,"rt") as select : tab_reader = csv.reader(select,delimiter="\t") for line in tab_reader : tab.append([line[i] for i in [0,1,2,3,4,5,6,11,13,14,18,19,20]]) os.remove(tab_path) #print("selected_tab ok") """ Supplementary ID to get from HUMAN_9606_idmapping.dat : -NextProt,BioGrid,STRING,KEGG """ if human : ids = ['neXtProt','BioGrid','STRING','KEGG' ] #ids to get from dat_file else : ids = ['BioGrid','STRING','KEGG' ] unidict = {} #keep only ids of interest in dictionaries dat_file=species_dict[species]+"_"+files[1] dat_path = download_from_uniprot_ftp(dat_file,target_directory) with gzip.open(dat_path,"rt") as dat : dat_reader = csv.reader(dat,delimiter="\t") for line in dat_reader : uniprotID=line[0] #UniProtID as key id_type=line[1] #ID type of corresponding id, key of sub-dictionnary cor_id=line[2] #corresponding id if "-" not in id_type : #we don't keep isoform if id_type in ids and uniprotID in unidict : if id_type in unidict[uniprotID] : unidict[uniprotID][id_type]= ";".join([unidict[uniprotID][id_type],cor_id]) #if there is already a value in the dictionnary else : unidict[uniprotID].update({ id_type : cor_id }) elif id_type in ids : unidict[uniprotID]={id_type : cor_id} os.remove(dat_path) #print("dat_file ok") #add ids from idmapping.dat to the final tab for line in tab[1:] : uniprotID=line[0] if human : if uniprotID in unidict : nextprot = access_dictionary(unidict,uniprotID,'neXtProt') if nextprot != '' : nextprot = clean_nextprot_id(nextprot,line[0]) line.extend([nextprot,access_dictionary(unidict,uniprotID,'BioGrid'),access_dictionary(unidict,uniprotID,'STRING'), access_dictionary(unidict,uniprotID,'KEGG')]) else : line.extend(["","","",""]) else : if uniprotID in unidict : line.extend([access_dictionary(unidict,uniprotID,'BioGrid'),access_dictionary(unidict,uniprotID,'STRING'), access_dictionary(unidict,uniprotID,'KEGG')]) else : line.extend(["","",""]) #print ("tab ok") #add missing nextprot ID for human if human : #build next_dict nextprot_ids = id_list_from_nextprot_ftp("nextprot_ac_list_all.txt",target_directory) next_dict = {} for nextid in nextprot_ids : next_dict[nextid.replace("NX_","")] = nextid os.remove(os.path.join(target_directory,"nextprot_ac_list_all.txt")) #add missing nextprot ID for line in tab[1:] : uniprotID=line[0] nextprotID=line[13] if nextprotID == '' and uniprotID in next_dict : line[13]=next_dict[uniprotID] output_file = species+"_id_mapping_"+ time.strftime("%d-%m-%Y") + ".tsv" path = os.path.join(target_directory,output_file) with open(path,"w") as out : w = csv.writer(out,delimiter='\t') w.writerows(tab) name_dict={"human" : "Homo sapiens", "mouse" : "Mus musculus", "rat" : "Rattus norvegicus"} name = name_dict[species]+" ("+time.strftime("%d-%m-%Y")+")" data_table_entry = dict(value = species+"_id_mapping_"+ time.strftime("%d-%m-%Y"), name = name, path = path) _add_data_table_entry(data_manager_dict, data_table_entry, "id_mapping") def download_from_uniprot_ftp(file,target_directory) : ftp_dir = "pub/databases/uniprot/current_release/knowledgebase/idmapping/by_organism/" path = os.path.join(target_directory, file) ftp = ftplib.FTP("ftp.uniprot.org") ftp.login("anonymous", "anonymous") ftp.cwd(ftp_dir) ftp.retrbinary("RETR " + file, open(path, 'wb').write) ftp.quit() return (path) def id_list_from_nextprot_ftp(file,target_directory) : ftp_dir = "pub/current_release/ac_lists/" path = os.path.join(target_directory, file) ftp = ftplib.FTP("ftp.nextprot.org") ftp.login("anonymous", "anonymous") ftp.cwd(ftp_dir) ftp.retrbinary("RETR " + file, open(path, 'wb').write) ftp.quit() with open(path,'r') as nextprot_ids : nextprot_ids = nextprot_ids.read().splitlines() return (nextprot_ids) #return '' if there's no value in a dictionary, avoid error def access_dictionary (dico,key1,key2) : if key1 in dico : if key2 in dico[key1] : return (dico[key1][key2]) else : return ("") #print (key2,"not in ",dico,"[",key1,"]") else : return ('') #if there are several nextprot ID for one uniprotID, return the uniprot like ID def clean_nextprot_id (next_id,uniprotAc) : if len(next_id.split(";")) > 1 : tmp = next_id.split(";") if "NX_"+uniprotAc in tmp : return ("NX_"+uniprotAc) else : return (tmp[1]) else : return (next_id) ####################################################################################################### # Main function ####################################################################################################### def main(): parser = argparse.ArgumentParser() parser.add_argument("--hpa", metavar = ("HPA_OPTION")) parser.add_argument("--peptideatlas", metavar=("SAMPLE_CATEGORY_ID")) parser.add_argument("--id_mapping", metavar = ("ID_MAPPING_SPECIES")) parser.add_argument("-o", "--output") args = parser.parse_args() data_manager_dict = {} # Extract json file params filename = args.output params = from_json_string(open(filename).read()) target_directory = params[ 'output_data' ][0]['extra_files_path'] os.mkdir(target_directory) ## Download source files from HPA try: hpa = args.hpa except NameError: hpa = None if hpa is not None: #target_directory = "/projet/galaxydev/galaxy/tools/proteore/ProteoRE/tools/resources_building/test-data/" hpa = hpa.split(",") for hpa_tissue in hpa: HPA_sources(data_manager_dict, hpa_tissue, target_directory) ## Download source file from Peptide Atlas query try: peptide_atlas = args.peptideatlas except NameError: peptide_atlas = None if peptide_atlas is not None: #target_directory = "/projet/galaxydev/galaxy/tools/proteore/ProteoRE/tools/resources_building/test-data/" peptide_atlas = peptide_atlas.split(",") for pa_tissue in peptide_atlas: peptide_atlas_sources(data_manager_dict, pa_tissue, target_directory) ## Download ID_mapping source file from Uniprot try: id_mapping=args.id_mapping except NameError: id_mapping = None if id_mapping is not None: id_mapping = id_mapping .split(",") for species in id_mapping : id_mapping_sources(data_manager_dict, species, target_directory) #save info to json file filename = args.output open(filename, 'wb').write(to_json_string(data_manager_dict)) if __name__ == "__main__": main()